On the other side of the spectrum, synthetic data and simulators today are commonly used but are very limited as they suffer from scalability and/or photorealism. Researchers and practitioners have extensively used these data as a benchmark to evaluate the depth, optical flow, etc. due to their perfect ground truth annotation. Most of these simulators do not support a realistic camera model or ray-tracing ability to render high-quality photorealistic images. Although, few approaches have proven to narrow down the generalization gap using synthetic data [1]}, [2]} for geometric-based quantities such as depth and optical flow as opposed to texture-centric approaches like semantic segmentation.
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